plot.iqrL: Plot Quantile Regression Coefficients with Longitudinal Data
In qrcm: Quantile Regression Coefficients Modeling

Description Usage Arguments Author(s) See Also Examples

Plots quantile regression coefficients β(u) and γ(v), based on a fitted model of class “iqrL”.

1 2	## S3 method for class 'iqrL' plot(x, conf.int = TRUE, polygon = TRUE, which = NULL, ask = TRUE, ...)

`x`	an object of class “`iqrL`”, the result of a call to `iqrL`.
`conf.int`	logical. If `TRUE`, asymptotic 95% confidence intervals are added to the plot.
`polygon`	logical. If `TRUE`, confidence intervals are represented by shaded areas via `polygon`. Otherwise, dashed lines are used.
`which`	an optional numerical vector indicating which coefficient(s) to plot. If `which = NULL`, all coefficients are plotted.
`ask`	logical. If `which = NULL` and `ask = TRUE` (the default), you will be asked interactively which coefficients to plot. Additional options will permit creating Q-Q plots of `u` or `v`, which should be independently distributed according to a Uniform(0,1) distribution. The option `ppplot(u,v)` will generate a P-P plot that compares the empirical distribution of `(u,v)` with its theoretical value, `F(u,v) = uv`, at a discrete grid of points.
`...`	additional graphical parameters, that can include `xlim, ylim, xlab, ylab, col, lwd, cex.lab, cex.axis, axes, frame.plot`. See `par`.

Paolo Frumento paolo.frumento@unipi.it

iqrL for model fitting; summary.iqrL and predict.iqrL for model summary and prediction.

  
  # using simulated data
  
  n <- 1000 # n. of observations
  n.id <- 100 # n. of clusters
  id <- rep(1:n.id, each = n/n.id) # cluster id

  x1 <- runif(n) # a level-1 covariate
  z1 <- rnorm(n.id) # a level-2 covariate

  V <- runif(n.id) # V_i
  U <- runif(n) # U_it

  alpha <- 2*(V - 1) + z1 # alpha
  y_alpha <- 1 + 2*qnorm(U) + 3*U*x1 # y - alpha
  y <- y_alpha + alpha[id] # observed outcome
  mydata <- data.frame(id = id, y = y, x1 = x1, z1 = z1[id])

  model <- iqrL(fx = y ~ x1, fu = ~ I(qnorm(u)) + u, 
    fz = ~ z1, fv = ~ -1 + I(qnorm(v)), id = id, data = mydata)
  par(mfrow = c(2,2))
  plot(model, ask = FALSE)